Keywords: Machine Learning/Artificial Intelligence, Data ProcessingDisease activity varies between patients with multiple sclerosis (MS), and patients who have a greater risk of developing a progressive course require more aggressive therapies earlier. However, differentiating disease severity is challenging using conventional methods as the disease often progresses silently. By taking advantage of one of the most advanced quantitative methods, convolutional neural networks, we aim to develop a new deep learning model to differentiate two common MS subtypes: relapsing-remitting course from secondary progressive phenotype. This study focuses on varying image pre-processing techniques and using different data views using conventional brain MRI.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords